1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ",", big.mark = "."),
                   caption="Tabla 1. Gastos Casa (últimos 30 registros)", align =rep('c', 3)) %>%
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
    kableExtra::scroll_box(width = "100%", height = "300px")
Tabla 1. Gastos Casa (últimos 30 registros)
fecha gasto monto gastador obs
7/6/2022 Comida 23.450 Andrés NA
13/6/2022 Comida 57.775 Tami NA
18/6/2022 Gas 81.350 Andrés NA
19/6/2022 VTR 21.990 Andrés NA
20/6/2022 Electricidad 67.655 Andrés NA
21/6/2022 Comida 38.000 Andrés NA
21/6/2022 Comida 15.000 Andrés Flor de loto verduras
24/6/2022 Comida 40.400 Andrés Bar la Providencia
27/6/2022 Agua 12.502 Andrés PAC AGUAS ANDIN 000000005687837
29/6/2022 Netflix 8.320 Tami NA
29/6/2022 Comida 68.213 Tami NA
30/6/2022 Comida 15.310 Tami NA
30/6/2022 Electricidad 67.655 Andrés NA
2/7/2022 Diosi 35.990 Andrés NA
3/7/2022 Gas 19.600 Andrés NA
3/7/2022 Parafina 44.029 Tami NA
11/7/2022 Diosi 15.930 Tami NA
11/7/2022 Comida 60.660 Tami NA
14/7/2022 Enceres 18.990 Andrés NA
15/7/2022 Ropa 18.990 Andrés NA
15/7/2022 Ropa 18.990 Andrés NA
15/7/2022 Comida 15.000 Andrés NA
19/7/2022 Parafina 22.521 Tami NA
20/7/2022 VTR 21.990 Andrés NA
21/7/2022 Comida 24.660 Andrés NA
23/7/2022 Enceres 14.315 Andrés NA
23/7/2022 Comida 22.263 Andrés NA
20/7/2022 Comida 41.830 Andrés NA
31/3/2019 Comida 9.000 Andrés NA
8/9/2019 Comida 24.588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 4.1420e+08   2    4.6024 0.0105 *  
## lag_depvar    7.3976e+10   1 1643.9759 <2e-16 ***
## Residuals     2.1014e+10 467                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838   863.433 13594.24 0.0213702
## 2-0 27969.635 22076.415 33862.85 0.0000000
## 2-1 20740.797 17147.407 24334.19 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
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## 21   20390.57             0   20986.00
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## 225  85013.86             2   91884.00
## 226  84535.29             2   85013.86
## 227  80700.43             2   84535.29
## 228  79740.57             2   80700.43
## 229  85163.14             2   79740.57
## 230  86724.86             2   85163.14
## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   315 50203.90 16312.166
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2072.363134   4063.287363   -560.783693   2418.382449  -3014.543070 
##             7             8             9            10            11 
##    502.422025  -5679.849503  -1160.598978  -3936.090755   -359.346216 
##            12            13            14            15            16 
##  -4889.445802  -1523.956565   -814.833103    455.276625  -3183.279373 
##            17            18            19            20            21 
##   -300.219562  -2063.518385   6677.475182  -1532.173260  -1201.442768 
##            22            23            24            25            26 
##   1488.045686  -1194.517432    233.979175   1687.328335  -7129.550489 
##            27            28            29            30            31 
##    985.470883   8211.867437    353.529366    -79.194231  -2462.191500 
##            32            33            34            35            36 
##   1540.146819   4521.424966   1034.577977   2295.427087  -1979.108470 
##            37            38            39            40            41 
##   4523.638646   4254.044049  -2359.657724  -3033.792680  -1128.270222 
##            42            43            44            45            46 
## -10747.252971   7384.800886   2571.742469   1355.066977   8081.627622 
##            47            48            49            50            51 
##    589.039050   6437.066871   6572.547702  -6069.748693  -4904.309678 
##            52            53            54            55            56 
##  -5110.178319  -7925.483956   6206.626917  -4067.494659  -4848.456352 
##            57            58            59            60            61 
##   3941.471819    926.030749     -7.501051    163.619813  -4979.487512 
##            62            63            64            65            66 
##  18188.134799   3523.525838  -3783.048396   5839.624753   7213.123447 
##            67            68            69            70            71 
##  14455.190432   1394.877561 -13489.560480  -1424.078952   4552.462993 
##            72            73            74            75            76 
##  -5023.798047  -4466.666028 -10510.567938   2554.046611  -5346.967638 
##            77            78            79            80            81 
##   1160.547495  -6791.958232    676.313254  -2246.917614  -2577.855779 
##            82            83            84            85            86 
##  -3805.361097   -390.141997   2448.189679   3857.224796    523.605962 
##            87            88            89            90            91 
##   -448.680060    232.083998   4330.646735  -1178.937825   1147.492226 
##            92            93            94            95            96 
##  -2078.669168  -1037.621884    192.870980    286.069410  -7477.166938 
##            97            98            99           100           101 
##   2468.100346  -8558.687133  -2821.231328  -3907.525260  -1583.360975 
##           102           103           104           105           106 
##  -1111.016064   3324.080477  -2247.343143   2698.606353  -1091.888326 
##           107           108           109           110           111 
##   1039.186410   2637.609598  -3135.085049  -4676.719961   -765.438726 
##           112           113           114           115           116 
##   1985.368371  11746.727646  -1307.065907   2623.571027   4197.986709 
##           117           118           119           120           121 
##   3405.393026  -1218.364407  -4809.709090  -3761.394032   2322.102205 
##           122           123           124           125           126 
##  -1752.837135   1338.869220   8843.914023    750.536403     37.755554 
##           127           128           129           130           131 
##  -2603.801607   2606.473014   6984.652887    886.161478  -8619.636814 
##           132           133           134           135           136 
##   1724.142158   4096.739381  -3237.271119  -1454.233192   -871.041386 
##           137           138           139           140           141 
##  -3887.177476   1213.137367   -480.697738  -2896.341424   1760.458948 
##           142           143           144           145           146 
##  -1860.714002  -7794.070754   2143.927233  -3408.077958   2197.341671 
##           147           148           149           150           151 
##   -194.658605   1079.933224   -319.672414   1389.802820   1206.290780 
##           152           153           154           155           156 
##   3362.119675  -4889.097281  -1152.740497  -3206.094565   6012.908787 
##           157           158           159           160           161 
##   9739.012570  -3117.263276  -4445.407344   3965.734350    502.205596 
##           162           163           164           165           166 
##   2988.300937  -5655.775766  -6443.414586   4511.875754  17688.036703 
##           167           168           169           170           171 
##   3737.340878   -314.749211  -2344.971757   -967.893256   3744.328154 
##           172           173           174           175           176 
##   -105.362476  -7942.065725   3083.440666   4512.910468    770.209573 
##           177           178           179           180           181 
##   8894.188886  -9185.986513  -3302.239310 -10538.722020 -10930.603145 
##           182           183           184           185           186 
##   1639.876392   9658.426704  -1179.796805   6184.506260   6742.162722 
##           187           188           189           190           191 
##  13277.051256   8421.922090  -4137.374485   2459.283873  10357.276753 
##           192           193           194           195           196 
##  -1740.720363  -2494.367209 -10280.401120  -6238.829162   1427.658139 
##           197           198           199           200           201 
##  -5055.288021  -9565.715729   5706.884785  -2818.241840  -1440.413827 
##           202           203           204           205           206 
##   -526.795064   6766.843827  10073.208586    661.427930   3010.357877 
##           207           208           209           210           211 
##   3161.856693   5827.561460  12830.919515  -5803.568475 -11318.887284 
##           212           213           214           215           216 
##  -5548.415856 -10404.166722  -4783.250701   1857.043954 -12715.282637 
##           217           218           219           220           221 
##  16805.182495   8023.766017   1654.240511  26802.482613  12364.368242 
##           222           223           224           225           226 
##   7079.410149  13743.973463  -4290.252508  -2010.978501   3578.558445 
##           227           228           229           230           231 
##    166.403327   2593.714287   8864.087179   5636.272078  -2112.981641 
##           232           233           234           235           236 
##  -1966.611749   9345.372844 -11655.517932  -7271.298530  -8433.804667 
##           237           238           239           240           241 
##  -9897.642021   3382.350893   1608.260189  -8065.482645  -8678.714385 
##           242           243           244           245           246 
##   9482.354575  -7499.810636   2822.359653 -10012.684109  -3672.232533 
##           247           248           249           250           251 
##   1820.536762   1357.304520 -11995.604216   4074.014076   2421.702601 
##           252           253           254           255           256 
##   4524.713920   2384.071648   -945.231912  11359.529070  20973.832118 
##           257           258           259           260           261 
##   3070.965582  -4399.464661   4060.305804  -1764.094915   3712.433778 
##           262           263           264           265           266 
##  -4895.450974 -10861.513369  -4564.822085   -308.429129  -4976.872966 
##           267           268           269           270           271 
##   9037.841123  -4130.170687   4384.917279  -1963.782084   4596.522081 
##           272           273           274           275           276 
##    822.930155   7411.917600  -1380.358456  12084.487427  -4650.130373 
##           277           278           279           280           281 
##   1732.852602   -369.364991   7873.866377  -5109.528605  -2703.940966 
##           282           283           284           285           286 
## -11189.423150  -2461.196004  18885.977572   7793.133414   2670.446265 
##           287           288           289           290           291 
##   -699.692217    866.630211   6369.382422   6800.203815 -18907.099286 
##           292           293           294           295           296 
## -11023.386522  -7871.409565   9999.097517   3274.278522  -1017.777487 
##           297           298           299           300           301 
##  27576.241302   9917.493247   4670.451455   9275.390013   2549.645119 
##           302           303           304           305           306 
##  -1317.950785   7675.475187 -24563.797123  -3464.262857    -50.490210 
##           307           308           309           310           311 
##  -6835.284506  -3751.010768   3195.350843  -8973.676354  -2906.128477 
##           312           313           314           315           316 
##  -7839.619687   1991.451556  -2772.217150   2441.521740  -3739.733393 
##           317           318           319           320           321 
##  27816.623655   -727.049107   3314.305104  10828.421379   5474.420985 
##           322           323           324           325           326 
##  32230.317442   4608.792207 -21418.871579   1642.966843    978.027406 
##           327           328           329           330           331 
##  -6574.211944  -1730.915879 -33222.354652   1384.014376  -1841.326981 
##           332           333           334           335           336 
##    369.982370  -2730.269845   4537.925662    -61.805326  -6589.633950 
##           337           338           339           340           341 
##  -2683.743994  -1744.442727  -7231.086560   4369.200030   -936.956179 
##           342           343           344           345           346 
##  -1311.870368   -570.617117    588.282259    868.238732  -1258.270731 
##           347           348           349           350           351 
##  -9083.744539 -12747.477094   2907.723370  -3800.158341  -3117.801853 
##           352           353           354           355           356 
##  -5431.872939   2334.302356   1901.160821   3214.884747  -3371.453934 
##           357           358           359           360           361 
##    -97.619714   1076.091407   7379.590203    532.466374    206.618642 
##           362           363           364           365           366 
##   2822.426518  -2548.965717   -640.282852  -8498.085540  -4270.558223 
##           367           368           369           370           371 
##  -5812.611996  -4489.953760  -6756.812086   5572.062057    821.167064 
##           372           373           374           375           376 
##   7535.797721  -7338.371898  -1875.865848  -2991.318310  -2047.223086 
##           377           378           379           380           381 
## -12029.091971   2468.453404 -10134.907003   6299.795860   9817.952663 
##           382           383           384           385           386 
##   3451.591845  -2130.060044   1895.762407   7002.505683  11571.775707 
##           387           388           389           390           391 
##  -5790.572529  -5258.967800     23.754903   8745.926733   1883.432510 
##           392           393           394           395           396 
##  11277.806240  -9958.695802   2850.507061    762.623209    616.549539 
##           397           398           399           400           401 
##   -593.714042   -481.499359 -14387.577643   8829.670326   -995.779353 
##           402           403           404           405           406 
##  -1168.508457   7204.764250  -7804.892949  -1052.964980  -2272.024416 
##           407           408           409           410           411 
##  -5528.652130  -2496.358446  -3530.483146  -8333.662107   6650.490220 
##           412           413           414           415           416 
##   2040.064752  -7017.849419  -7253.716255  14737.431369   4101.645505 
##           417           418           419           420           421 
##   4712.191534  -7881.971814  -4476.278748  -2274.360274   3170.120722 
##           422           423           424           425           426 
## -13713.855537  -2314.000022  -8612.618625   3591.674094   7472.865933 
##           427           428           429           430           431 
##   6942.515426  -3729.247033  -3814.383267  -4370.421908  -1389.353883 
##           432           433           434           435           436 
##  -5308.439721  -6167.731726  -5429.529209   -828.470250   -304.977114 
##           437           438           439           440           441 
##  -4459.246113   3129.081716   5312.703972  -4679.817729  -1732.562237 
##           442           443           444           445           446 
##   2006.431443  -3453.490101   3251.137023  -6224.113982 -11684.197911 
##           447           448           449           450           451 
##  -3947.402641  10229.373240  -1618.687305   5172.751883  -5537.136695 
##           452           453           454           455           456 
##   -724.346564    777.240749   3395.242479 -11956.265267   3833.763415 
##           457           458           459           460           461 
##  -6309.008640   6983.758359   3360.042409   2800.765183  -3593.008701 
##           462           463           464           465           466 
##   2393.623392    257.458701   2053.678471   -287.980309   3590.019198 
##           467           468           469           470           471 
##  -2445.131561   6038.947433  -6784.554819  -2704.336388  -1907.197292 
##           472 
##  -4342.009763 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17196.92 20075.71 24376.93 24091.76 26471.26 23774.29 24498.56 19677.74 
##       10       11       12       13       14       15       16       17 
## 19411.38 16724.63 17510.73 14203.81 14255.55 14927.58 16642.99 14944.36 
##       18       19       20       21       22       23       24       25 
## 15990.52 15357.10 22518.17 21592.01 21066.10 22977.09 22295.59 22955.39 
##       26       27       28       29       30       31       32       33 
## 24821.84 18682.81 20428.13 28352.47 28410.77 28080.05 25683.14 27101.15 
##       34       35       36       37       38       39       40       41 
## 30986.85 31339.14 32763.97 30246.93 34188.96 37432.66 34456.08 31231.56 
##       42       43       44       45       46       47       48       49 
## 30066.54 20541.48 28143.69 30607.22 31708.52 38622.53 38111.50 42825.45 
##       50       51       52       53       54       55       56       57 
## 47108.75 39725.60 34233.75 29201.20 22269.52 28629.35 25172.03 21428.53 
##       58       59       60       61       62       63       64       65 
## 25885.83 27159.36 27459.67 27876.06 23701.15 40476.62 42341.05 37534.23 
##       66       67       68       69       70       71       72       73 
## 41787.88 46758.10 57544.69 55536.42 40615.79 38093.97 41145.37 35382.24 
##       74       75       76       77       78       79       80       81 
## 30784.00 21384.24 24621.25 20501.74 22610.96 17449.83 19487.63 18705.57 
##       82       83       84       85       86       87       88       89 
## 17722.50 15770.00 17061.95 20710.06 25176.82 26177.68 26202.92 26826.50 
##       90       91       92       93       94       95       96       97 
## 30997.37 29814.94 30825.38 28868.34 28059.27 28431.50 28842.60 22348.76 
##       98       99      100      101      102      103      104      105 
## 25397.26 18350.37 17193.81 15212.79 15515.87 16200.78 20723.06 19796.39 
##      106      107      108      109      110      111      112      113 
## 23346.46 23134.10 24828.82 27737.51 25207.86 21611.87 21890.35 24565.99 
##      114      115      116      117      118      119      120      121 
## 35551.07 33723.86 35581.73 38613.32 40590.94 38253.71 33017.25 29318.04 
##      122      123      124      125      126      127      128      129 
## 31423.98 29684.85 30879.51 38563.61 38202.10 37253.23 34081.96 35882.92 
##      130      131      132      133      134      135      136      137 
## 41340.70 40774.78 31878.86 33157.69 36382.84 32753.66 31123.04 30197.89 
##      138      139      140      141      142      143      144      145 
## 26716.72 28146.84 27913.91 25574.54 27621.43 26230.93 19762.07 22826.22 
##      146      147      148      149      150      151      152      153 
## 20628.80 23638.94 24184.92 25792.96 25977.05 27649.57 28964.74 32030.53 
##      154      155      156      157      158      159      160      161 
## 27450.45 26705.24 24233.38 30192.84 41137.69 39449.41 36785.12 41861.08 
##      162      163      164      165      166      167      168      169 
## 43285.27 46739.06 42154.70 37409.84 42895.25 59378.23 61614.89 60011.40 
##      170      171      172      173      174      175      176      177 
## 56801.89 55183.39 57915.93 56929.21 49135.85 51990.66 55774.79 55811.38 
##      178      179      180      181      182      183      184      185 
## 63019.27 53416.24 50131.15 40837.89 32283.41 35830.57 46046.08 45496.07 
##      186      187      188      189      190      191      192      193 
## 51514.84 57323.52 68226.08 73567.52 67192.29 67387.87 74536.58 70165.08 
##      194      195      196      197      198      199      200      201 
## 65638.26 54762.83 48726.77 50166.86 45712.72 37794.69 44290.67 42498.41 
##      202      203      204      205      206      207      208      209 
## 42132.37 42616.01 49485.36 58473.14 58098.64 59842.57 61516.72 65349.94 
##      210      211      212      213      214      215      216      217 
## 74921.43 66916.46 54974.56 49523.60 40420.11 37344.10 40492.28 30401.82 
##      218      219      220      221      222      223      224      225 
## 47563.52 54965.47 55877.37 78895.20 86473.30 88498.74 96174.25 87024.84 
##      226      227      228      229      230      231      232      233 
## 80956.73 80534.03 77146.86 76299.06 81088.59 82467.98 76841.75 72001.63 
##      234      235      236      237      238      239      240      241 
## 77717.95 64217.73 56165.95 48027.36 39545.93 43784.31 45960.91 39339.00 
##      242      243      244      245      246      247      248      249 
## 32948.50 43344.95 37528.07 41507.40 33685.52 32377.03 36072.84 38928.03 
##      250      251      252      253      254      255      256      257 
## 29655.84 35659.73 39503.29 44755.64 47504.09 46991.04 57406.17 75097.32 
##      258      259      260      261      262      263      264      265 
## 74910.32 68146.84 69645.09 65823.99 67286.17 60974.66 50130.39 46113.71 
##      266      267      268      269      270      271      272      273 
## 46325.44 42389.02 51290.74 47522.51 51715.21 49810.91 53923.36 54222.65 
##      274      275      276      277      278      279      280      281 
## 60306.79 57914.80 67694.99 61552.43 61764.79 60095.56 65902.10 59563.08 
##      282      283      284      285      286      287      288      289 
## 56088.85 45525.34 43904.31 61327.58 66918.98 67332.98 64721.94 63799.19 
##      290      291      292      293      294      295      296      297 
## 67844.51 71798.10 52583.96 42576.27 36520.90 46956.72 50234.49 49338.62 
##      298      299      300      301      302      303      304      305 
## 73803.22 79814.55 80489.61 85153.21 83331.81 78306.95 81812.23 56432.69 
##      306      307      308      309      310      311      312      313 
## 52652.35 52328.57 46049.87 43228.36 46871.68 39341.27 38049.19 32550.41 
##      314      315      316      317      318      319      320      321 
## 36376.93 35549.19 39423.16 37385.23 63457.62 61274.84 62916.44 71003.29 
##      322      323      324      325      326      327      328      329 
## 73417.11 99181.49 97541.16 73103.18 71887.69 70226.78 62089.20 59179.50 
##      330      331      332      333      334      335      336      337 
## 28794.41 32522.90 32967.30 35312.98 34646.50 40477.52 41565.06 36759.89 
##      338      339      340      341      342      343      344      345 
## 35965.59 36093.66 31360.66 37426.24 38097.01 38358.33 39243.86 41049.62 
##      346      347      348      349      350      351      352      353 
## 42891.84 42640.74 35507.05 25970.13 31374.16 30222.52 29808.02 27397.98 
##      354      355      356      357      358      359      360      361 
## 32128.84 35924.83 40438.03 38606.91 39881.19 42043.41 49520.82 50077.52 
##      362      363      364      365      366      367      368      369 
## 50281.43 52771.97 50227.43 49665.80 42229.27 39394.90 35529.38 33283.38 
##      370      371      372      373      374      375      376      377 
## 29297.37 36666.26 38978.63 46951.80 40856.44 40297.46 38818.51 38346.09 
##      378      379      380      381      382      383      384      385 
## 29112.26 33761.48 26735.92 35046.62 45494.55 49099.63 47353.81 49367.64 
##      386      387      388      389      390      391      392      393 
## 55656.94 65247.86 58383.68 52790.39 52516.07 59977.71 60506.91 69271.98 
##      394      395      396      397      398      399      400      401 
## 58256.49 59840.81 59396.02 58874.14 57344.21 56092.01 42703.33 51384.49 
##      402      403      404      405      406      407      408      409 
## 50373.79 49328.52 55801.04 48260.54 47564.02 45872.08 41501.22 40318.91 
##      410      411      412      413      414      415      416      417 
## 38361.23 32389.65 40350.08 43308.99 37922.00 32955.57 47992.78 51880.38 
##      418      419      420      421      422      423      424      425 
## 55853.40 48238.71 44521.07 43182.31 46808.71 35098.86 34825.05 29019.90 
##      426      427      428      429      430      431      432      433 
## 34671.99 43092.34 50061.25 46790.67 43826.71 40717.64 40604.58 37043.16 
##      434      435      436      437      438      439      440      441 
## 33138.53 30341.76 31935.41 33805.39 31787.78 36708.15 42982.82 39698.99 
##      442      443      444      445      446      447      448      449 
## 39401.71 42441.63 40304.15 44338.11 39532.06 30464.40 29288.91 40772.40 
##      450      451      452      453      454      455      456      457 
## 40450.39 46164.57 41752.06 42105.62 43744.19 47503.84 37265.24 42168.58 
##      458      459      460      461      462      463      464      465 
## 37540.81 45194.24 48753.52 51403.29 48096.38 50463.26 50667.04 52433.55 
##      466      467      468      469      470      471      472 
## 51925.55 54902.13 52200.62 57308.13 50492.91 48077.20 46647.58 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8579
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    4.602357  0.5727316    2.932086
## t2* 1643.975933 30.1672587  250.781792
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.278922       4.705285   10.56854
## 2    lag_depvar 1289.983774    1657.752186 2116.40933

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Jul 25 00:49:19 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jul 25 00:49:26 2022
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## =-=-=-=-= Iteration 4000 Mon Jul 25 00:49:32 2022
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## =-=-=-=-= Iteration 6000 Mon Jul 25 00:49:39 2022
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## =-=-=-=-= Iteration 8000 Mon Jul 25 00:49:46 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jul 25 00:49:53 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jul 25 00:50:00 2022
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## =-=-=-=-= Iteration 14000 Mon Jul 25 00:50:06 2022
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##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3) %>% 
  knitr::kable(format="html", caption="Tabla. Gastos promedio por ítem a contar del...",
               col.names= c("Item","2023","2022","2021","2020")) %>% 
  kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")
Tabla. Gastos promedio por ítem a contar del…
Item 2023 2022 2021 2020
Agua NA 7.328667 6.342333 7.780500
Comida NA 304.551500 313.448222 345.242400
Comunicaciones NA 0.000000 0.000000 0.000000
Electricidad NA 37.112000 32.052667 27.473433
Enceres NA 10.915000 13.505778 23.708267
Farmacia NA 3.663333 10.551833 11.945800
Gas/Bencina NA 54.006667 27.058000 23.138133
Diosi NA 13.517833 39.631167 38.627233
donaciones/regalos NA 0.000000 9.560111 9.157300
Electrodomésticos/ Mantención casa NA 7.888000 40.359333 27.648933
VTR NA 28.990000 22.387944 21.078267
Netflix NA 7.369500 7.142333 7.584300
Otros NA 6.302167 2.100722 1.260433
Total 0 481.644667 524.140444 544.645000
## Joining, by = "word"


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: 35 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1682, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2022-08-09 00:04:58 sería de: 34.530 pesos// Percentil 95% más alto proyectado: 37.582,14

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)

dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="html", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)")) %>% 
  kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 33599.83 33568.79
Lo.80 33689.29 33661.54
Point.Forecast 34529.69 36388.94
Hi.80 36218.05 41084.90
Hi.95 37144.98 43570.79


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.3280  985.7562
## s.e.  0.1535   38.4750
## 
## sigma^2 = 29191:  log likelihood = -267.98
## AIC=541.96   AICc=542.61   BIC=547.1
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1     xreg
##       0.3618  33.3818
## s.e.  0.1548   1.3979
## 
## sigma^2 = 30136:  log likelihood = -268.65
## AIC=543.3   AICc=543.94   BIC=548.44
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="html", caption="Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) %>% 
  kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 875.1395 631.2725 664.1008
Lo.80 1001.4686 753.9717 743.6058
Point.Forecast 1240.1098 985.7558 920.6852
Hi.80 1478.7510 1217.5400 1219.2415
Hi.95 1605.0801 1340.2391 1414.6897


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.2.7  bsts_0.9.8          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.10         MASS_7.3-54         scales_1.2.0       
##  [7] ggiraph_0.8.2       tidytext_0.3.3      DT_0.23            
## [10] autoplotly_0.1.4    rvest_1.0.2         plotly_4.10.0      
## [13] xts_0.12.1          forecast_8.16       wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.0     tm_0.7-8           
## [19] NLP_0.2-1           tsibble_1.1.1       forcats_0.5.1      
## [22] dplyr_1.0.9         purrr_0.3.4         tidyr_1.2.0        
## [25] tibble_3.1.8        ggplot2_3.3.6       tidyverse_1.3.2    
## [28] sjPlot_2.8.10       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.7.7      httr_1.4.3         
## [34] readxl_1.4.0        zoo_1.8-10          stringr_1.4.0      
## [37] stringi_1.7.8       DataExplorer_0.8.2  data.table_1.14.2  
## [40] reshape2_1.4.4      fUnitRoots_3042.79  fBasics_3042.89.2  
## [43] timeSeries_4021.104 timeDate_4021.104   plyr_1.8.7         
## [46] readr_2.1.2        
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2          tidyselect_1.1.2    lme4_1.1-30        
##   [4] htmlwidgets_1.5.4   munsell_0.5.0       codetools_0.2-18   
##   [7] effectsize_0.7.0    its.analysis_1.6.0  withr_2.5.0        
##  [10] colorspace_2.0-3    ggfortify_0.4.14    highr_0.9          
##  [13] knitr_1.39          uuid_1.1-0          rstudioapi_0.13    
##  [16] TTR_0.24.3          labeling_0.4.2      emmeans_1.7.5      
##  [19] slam_0.1-50         bit64_4.0.5         farver_2.1.1       
##  [22] datawizard_0.4.1    rprojroot_2.0.3     vctrs_0.4.1        
##  [25] generics_0.1.3      xfun_0.31           R6_2.5.1           
##  [28] bitops_1.0-7        cachem_1.0.6        assertthat_0.2.1   
##  [31] networkD3_0.4       vroom_1.5.7         nnet_7.3-16        
##  [34] googlesheets4_1.0.0 gtable_0.3.0        spatial_7.3-14     
##  [37] rlang_1.0.4         forge_0.2.0         systemfonts_1.0.4  
##  [40] splines_4.1.2       lazyeval_0.2.2      gargle_1.2.0       
##  [43] selectr_0.4-2       broom_1.0.0         yaml_2.3.5         
##  [46] abind_1.4-5         modelr_0.1.8        crosstalk_1.2.0    
##  [49] backports_1.4.1     quantmod_0.4.20     tokenizers_0.2.1   
##  [52] tools_4.1.2         ellipsis_0.3.2      gplots_3.1.3       
##  [55] kableExtra_1.3.4    jquerylib_0.1.4     Rcpp_1.0.9         
##  [58] base64enc_0.1-3     fracdiff_1.5-1      haven_2.5.0        
##  [61] fs_1.5.2            magrittr_2.0.3      lmtest_0.9-40      
##  [64] reprex_2.0.1        googledrive_2.0.0   mvtnorm_1.1-3      
##  [67] sjmisc_2.8.9        hms_1.1.1           evaluate_0.15      
##  [70] xtable_1.8-4        sjstats_0.18.1      ggeffects_1.1.2    
##  [73] compiler_4.1.2      KernSmooth_2.23-20  crayon_1.5.1       
##  [76] minqa_1.2.4         htmltools_0.5.3     tzdb_0.3.0         
##  [79] lubridate_1.8.0     DBI_1.1.3           sjlabelled_1.2.0   
##  [82] dbplyr_2.2.1        boot_1.3-28         Matrix_1.3-4       
##  [85] car_3.1-0           cli_3.3.0           quadprog_1.5-8     
##  [88] parallel_4.1.2      insight_0.18.0      igraph_1.3.4       
##  [91] pkgconfig_2.0.3     xml2_1.3.3          svglite_2.1.0      
##  [94] bslib_0.4.0         webshot_0.5.3       estimability_1.4   
##  [97] anytime_0.3.9       snakecase_0.11.0    janeaustenr_0.1.5  
## [100] digest_0.6.29       parameters_0.18.1   janitor_2.1.0      
## [103] rmarkdown_2.14      cellranger_1.1.0    curl_4.3.2         
## [106] gtools_3.9.3        urca_1.3-0          nloptr_2.0.3       
## [109] lifecycle_1.0.1     nlme_3.1-153        jsonlite_1.8.0     
## [112] tseries_0.10-51     carData_3.0-5       viridisLite_0.4.0  
## [115] fansi_1.0.3         pillar_1.8.0        fastmap_1.1.0      
## [118] glue_1.6.2          bayestestR_0.12.1   bit_4.0.4          
## [121] sass_0.4.2          performance_0.9.1   r2d3_0.2.6         
## [124] caTools_1.18.2
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Paquetes estadísticos utilizados')),
      options=list(
initComplete = htmlwidgets::JS(
      "function(settings, json) {",
      "$(this.api().tables().body()).css({'font-size': '80%'});",
      "}")))